Pattern recognition

Achieving the carbon intensity target of China: A least squares support vector machine with mixture kernel function approach

Person reidentification consists of recognizing individuals across different sensors of a camera network. Whereas clothing appearance cues are widely used, other modalities could be exploited as additional information sources, like anthropometric measures and gait. In this paper, we investigate whether the reidentification accuracy of clothing appearance descriptors can be improved by fusing them with anthropometric measures extracted from depth data, using RGB-D sensors, in unconstrained settings. We also propose a dissimilarity-based framework for building and fusing the multimodal descriptors of pedestrian images for reidentification tasks, as an alternative to the widely used score-level fusion. The experimental evaluation is carried out on two data sets including RGB-D data, one of which is a novel publicly available data set that we acquired using Kinect sensors. The fusion with anthropometric measures increases the first-rank recognition rate of clothing appearance descriptors up to 20%, whereas our fusion approach reduces the processing cost of the matching phase.

Multimodal Person Reidentification Using RGB-D Cameras

With the rapid development of mobile devices and pervasive computing technologies, acceleration-based human activity recognition, a difficult yet essential problem in mobile apps, has received intensive attention recently. Different acceleration signals for representing different activities or even a same activity have different attributes, which causes troubles in normalizing the signals. We thus cannot directly compare these signals with each other, because they are embedded in a nonmetric space. Therefore, we present a nonmetric scheme that retains discriminative and robust frequency domain information by developing a novel ensemble manifold rank preserving (EMRP) algorithm. EMRP simultaneously considers three aspects: 1) it encodes the local geometry using the ranking order information of intraclass samples distributed on local patches; 2) it keeps the discriminative information by maximizing the margin between samples of different classes; and 3) it finds the optimal linear combination of the alignment matrices to approximate the intrinsic manifold lied in the data. Experiments are conducted on the South China University of Technology naturalistic 3-D acceleration-based activity dataset and the naturalistic mobile-devices based human activity dataset to demonstrate the robustness and effectiveness of the new nonmetric scheme for acceleration-based human activity recognition.

Ensemble Manifold Rank Preserving for Acceleration-Based Human Activity Recognition

https://pralab.diee.unica.it/sites/default/files/Satta_PRL2012.pdf

Fast person re-identification based on dissimilarity representations

In this paper a novel approach for dissimilarity-based representation is presented, which combines local image descriptors with several dissimilarity functions. The basic idea consists of defining the set of prototypes in terms of local descriptors of image parts, namely feature points extracted from the training set. Therefore, according to the dissimilarity-based approach, a new image can be characterized on the basis of its dissimilarity with each of the given prototypes. This leads to a new class of Local Kernels which exploits the use of dissimilarities between image parts. In particular, we show that the classic Bag-of-Feature (BoF) kernel can be revised as a special case of our new formulation, and better performance can be obtained when new dissimilarity functions are employed. Moreover, we observe that any variants of the basic BoF kernel can take advantage from our approach as we show for the case of the Pyramid Match kernel. Promising results are shown for image categorization on the ETH-80 database.

http://profs.sci.univr.it/~castella/papers/pdf/CIP2010.pdf

Dissimilarity-based representation for local parts

Generative embeddings use generative probabilistic models to project objects into a vectorial space of reduced dimensionality - where the so-called generative kernels can be defined. Some of these approaches employ generative models on latent variables to project objects into a feature space where the dimensions are related to the latent variables. Here, we propose to enhance the discriminative power of such spaces by performing a non-linear mapping of space dimensions leading to the formulation of novel generative kernels. In this paper, we investigate one possible non-linear mapping, based on a powering operation, able to equilibrate the contributions of each latent variable of the model, thus augmenting the entropy of the latent variables vectors. The validity of the idea has been shown in the case of two generative kernels, which have been evaluated with tests on shape recognition and gesture classification, with really satisfying results that outperform state-of-the-art methods.

/pdf/non-linear-generative-embeddings-for-kernels-on-latent-50yija9hs3.pdf

Non-linear generative embeddings for kernels on latent variable models

http://profs.sci.univr.it/~bicego/papers/2014_Neurocomputing.pdf

Generative embeddings based on Rician mixtures for kernel-based classification of magnetic resonance images

Most approaches to classifier learning for structured objects (such as images or sequences) are based on probabilistic generative models. On the other hand, state-of-the-art classifiers for vectorial data are learned discriminatively. In recent years, these two dual paradigms have been combined via the use of generative embeddings (of which the Fisher kernel is arguably the best known example); these embeddings are mappings from the object space into a fixed dimensional score space, induced by a generative model learned from data, on which a (maybe kernel-based) discriminative approach can then be used. This paper proposes a new semi-parametric approach to build generative embeddings for classification of magnetic resonance images (MRI). Based on the fact that MRI data is well described by Rice distributions, we propose to use Rician mixtures as the underlying generative model, based on which several different generative embeddings are built. These embeddings yield vectorial representations on which kernel-based support vector machines (SVM) can be trained for classification. Concerning the choice of kernel, we adopt the recently proposed nonextensive information theoretic kernels. The methodology proposed was tested on a challenging classification task, which consists in classifying MRI images as belonging to schizophrenic or non-schizophrenic human subjects. The classification is based on a set of regions of interest (ROIs) in each image, with the classifiers corresponding to each ROI being combined via boosting. The experimental results show that the proposed methodology outperforms the previous state-of-the-art methods on the same dataset.

/pdf/generative-embeddings-based-on-rician-mixtures-application-3vpfs9x2qp.pdf

Generative embeddings based on Rician mixtures:Application to kernel-based discriminative classification of magnetic resonance images

Generative kernels have emerged in the last years as an effective method for mixing discriminative and generative approaches. In particular, in this paper, we focus on kernels defined on generative models with latent variables (e.g. the states in a Hidden Markov Model). The basic idea underlying these kernels is to compare objects, via a inner product, in a feature space where the dimensions are related to the latent variables of the model. Here we propose to enhance these kernels via a nonlinear normalization of the space, namely a nonlinear mapping of space dimensions able to exploit their discriminative characteristics. In this paper we investigate three possible nonlinear mappings, for two HMM-based generative kernels, testing them in different sequence classification problems, with really promising results.

/pdf/nonlinear-mappings-for-generative-kernels-on-latent-variable-5djxraa685.pdf

Anna C. Carli

Papers

Dissimilarity-based representation for local parts

Non-linear generative embeddings for kernels on latent variable models

Generative embeddings based on Rician mixtures for kernel-based classification of magnetic resonance images

Generative embeddings based on Rician mixtures:Application to kernel-based discriminative classification of magnetic resonance images

Nonlinear Mappings for Generative Kernels on Latent Variable Models